Author name cluster

Nicolas Gauthier

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2 papers

2 author rows

AAAI Conference 2021 System Paper

TAILOR: Teaching with Active and Incremental Learning for Object Registration

Qianli Xu
Nicolas Gauthier
Wenyu Liang
Fen Fang
Hui Li Tan
Ying Sun
Yan Wu
Liyuan Li

When deploying a robot to a new task, one often has to train it to detect novel objects, which is time-consuming and laborintensive. We present TAILOR - a method and system for object registration with active and incremental learning. When instructed by a human teacher to register an object, TAILOR is able to automatically select viewpoints to capture informative images by actively exploring viewpoints, and employs a fast incremental learning algorithm to learn new objects without potential forgetting of previously learned objects. We demonstrate the effectiveness of our method with a KUKA robot to learn novel objects used in a real-world gearbox assembly task through natural interactions.

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ICRA Conference 2021 Conference Paper

Towards Efficient Multiview Object Detection with Adaptive Action Prediction

Qianli Xu
Fen Fang
Nicolas Gauthier
Wenyu Liang
Yan Wu 0002
Liyuan Li
Joo Hwee Lim

Active vision is a desirable perceptual feature for robots. Existing approaches usually make strong assumptions about the task and environment, thus are less robust and efficient. This study proposes an adaptive view planning approach to boost the efficiency and robustness of active object detection. We formulate the multi-object detection task as an active multiview object detection problem given the initial location of the objects. Next, we propose a novel adaptive action prediction (A2P) method built on a deep Q-learning network with a dueling architecture. The A2P method is able to perform view planning based on visual information of multiple objects; and adjust action ranges according to the task status. Evaluated on the AVD dataset, A2P leads to 21. 9% increase in detection accuracy in unfamiliar environments, while improving efficiency by 22. 7%. On the T-LESS dataset, multi-object detection boosts efficiency by more than 30% while achieving equivalent detection accuracy.

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